Checkpoint 1: EDA

By Arturo, Payson, and Victoria

Does healthcare access affect the number of preventable hospital stays of certain racial groups at the county level?

Why this matters:

  • Reduce preventable hospitalizations
  • Equitable resource allocation
  • Focus efforts where help is needed most

National Data

Source: County Health Rankings 2025

Raw Data:

  • County-level health outcomes(ex)
  • Healthcare access variables(ex)
  • Disaggregated by race

Cleaned Data:

  • Filtered and cleaned to complete observations
  • Created “White Majority” vs. “Minority” indicator

Preventable Hospitalizations by County

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Regression Analysis of Racial Disparities in Preventable Hospital Stays

Planned Approach:

  • Linear regression with interaction terms
  • Outcome: Preventable hospital stays
  • Predictors: Uninsured rate, provider ratio, race group

Modeling Strategy:

  • Explore variable selection and regularization

Moving into modeling and interpretation

Completed:

  • Defined research question
  • Cleaned and preprocessed dataset
  • Created initial EDA visualizations

Next Steps:

  • Fit regression models with race × access interactions
  • Interpret disparities and validate model
  • Create visual summaries and poster
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